OpenRaiser/Intern-Atlas huggingface dataset:
Morning or Evening? An Examination of Circadian Rhythms of CS1 Students
Venue: arXiv cs.CY 2021
Year: 2021

Circadian rhythms are the cycles of our internal clock that play a key role in governing when we sleep and when we are active. A related concept is chronotype, which is a person's natural tendency toward activity at certain times of day and typically governs when the individual is most alert and productive. In this work we investigate chronotypes in the setting of an Introductory Computer Programming (CS1) course. Using keystroke data collected from students we investigate the existence of chronotypes through unsupervised learning. The chronotypes we find align with those of typical populations reported in the literature and our results support correlations of certain chronotypes to academic achievement. We also find a lack of support for the still-popular stereotype of a computer programmer as a night owl. The analyses are conducted on data from two universities, one in the US and one in Europe, that use different teaching methods. In comparison of the two contexts, we look into programming assignment design and administration that may promote better programming practices among students in terms of procrastination and effort.
Current eHealth Challenges and recent trends in eHealth applications
Venue: arXiv cs.CY 2021
Year: 2021

eHealth (Health Informatics/Medical Informatics) field is growing worldwide due to acknowledge of reputable Organizations such as World Health Organization, Institute of Medicine in USA and several others. This field is facing number of challenges and there is need to classify these challenges mentioned by different researchers of this area. The purpose of this study is to classify different eHealth challenges in broader categories. We also analyzed recent eHealth Applications to identify current trends of such applications. In this paper, we identify stakeholders who are responsible to contribute in a particular eHealth challenge. Through eHealth application analysis, we categories these applications based on different factors. We identify different socio-economic benefits, which these applications can provide. We also present ecosystem of an eHealth application. We gave recommendations for eHealth challenges relevant to Information Technology domain. We conclude our discussion by specifying areas for future research and recommending researchers to work on identify which type of disease can control and manage by different eHealth applications.
How Developers Engineer Test Cases: An Observational Study
Venue: arXiv cs.SE 2021
Year: 2021

One of the main challenges that developers face when testing their systems lies in engineering test cases that are good enough to reveal bugs. And while our body of knowledge on software testing and automated test case generation is already quite significant, in practice, developers are still the ones responsible for engineering test cases manually. Therefore, understanding the developers' thought- and decision-making processes while engineering test cases is a fundamental step in making developers better at testing software. In this paper, we observe 13 developers thinking-aloud while testing different real-world open-source methods, and use these observations to explain how developers engineer test cases. We then challenge and augment our main findings by surveying 72 software developers on their testing practices. We discuss our results from three different angles. First, we propose a general framework that explains how developers reason about testing. Second, we propose and describe in detail the three different overarching strategies that developers apply when testing. Third, we compare and relate our observations with the existing body of knowledge and propose future studies that would advance our knowledge on the topic.
Expectation-Maximization-Aided Hybrid Generalized Expectation Consistent for Sparse Signal Reconstruction
Venue: arXiv cs.IT 2021
Year: 2021

The reconstruction of sparse signal is an active area of research. Different from a typical i.i.d. assumption, this paper considers a non-independent prior of group structure. For this more practical setup, we propose EM-aided HyGEC, a new algorithm to address the stability issue and the hyper-parameter issue facing the other algorithms. The instability problem results from the ill condition of the transform matrix, while the unavailability of the hyper-parameters is a ground truth that their values are not known beforehand. The proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan et al.) but we replace its inner engine, the GAMP, by a matrix-insensitive alternative, the GEC, so that the first issue is solved. For the second issue, we take expectation-maximization as an outer loop, and together with the inner engine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the proposed algorithm is also verified by means of numerical simulations.
A Spectral Enabled GAN for Time Series Data Generation
Venue: arXiv cs.LG 2021
Year: 2021

Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying the training of the independent networks in TSGAN and creating a dependency both in training and learning. This improvement, called unified TSGAN (uTSGAN) was tested and comapred both quantitatively and qualitatively to its predecessor on 70 benchmark time series data sets used in the community. uTSGAN showed to outperform TSGAN in 80\% of the data sets by the same number of training epochs and 60\% of the data sets in 3/4th the amount of training time or less while maintaining the few shot generation ability with better FID scores across those data sets.
SLD-resolution without occur-check, an example
Venue: arXiv cs.LO 2021
Year: 2021

We prove that the occur-check is not needed for a certain definite clause logic program, independently from the selection rule. First we prove that the program is occur-check free. Then we consider a more general class of queries, under which the program is not occur-check free; however we show that it will be correctly executed under Prolog without occur-check. The main result of this report states that the occur-check may be skipped for the cases in which a single run of a standard nondeterministic unification algorithm does not fail due to the occur-check. The usual approaches are based on the notion of NSTO (not subject to occur-check), which considers all the runs. To formulate the result, it was necessary to introduce an abstraction of a "unification" algorithm without the occur-check.
Categorical Foundations of Gradient-Based Learning
Venue: arXiv cs.LG 2021
Year: 2021

We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as as MSE and Softmax cross-entropy, shedding new light on their similarities and differences. Our approach to gradient-based learning has examples generalising beyond the familiar continuous domains (modelled in categories of smooth maps) and can be realized in the discrete setting of boolean circuits. Finally, we demonstrate the practical significance of our framework with an implementation in Python.
COVID-19 vaccination strategies on dynamic networks
Venue: arXiv cs.SI 2021
Year: 2021

Coronavirus disease (COVID-19), which was caused by SARS-CoV-2, has become a global public health concern. A great proportion of the world needs to be vaccinated in order to stop the rapid spread of the disease. In addition to prioritising vulnerable sections of the population to receive the vaccine, an ideal degree-based vaccination strategy uses fine-grained contact networks to prioritise vaccine recipients. This strategy is costly and impractical due to the enormous amount of specific contact information needed. It also does not capture indirect famine or aerosol-based transmission. We recently proposed a new vaccination strategy called Individual's Movement-based Vaccination (IMV), which takes into account both direct and indirect transmission and is based on the types of places people visit. IMV was shown to be cost-efficient in the case of influenza-like diseases. This paper studies the application of IMV to COVID-19 using its documented transmission parameters. We conduct large scale computer simulations based on a city-wide empirical mobility dataset to evaluate the performance and practicability of the strategy. Results show that the proposed strategy achieves nearly five times the efficiency of random vaccination and performs comparably to the degree-based strategy, while significantly reducing the data collection requirements.
PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics
Venue: arXiv cs.CV 2021
Year: 2021

Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring requires determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Methods. Herein, we aimed to use machine learning to identify, quantitate and diagnose EoE. We labeled more than 100M pixels of 4345 images obtained by scanning whole slides of H&E-stained sections of esophageal biopsies derived from 23 EoE patients. We used this dataset to train a multi-label segmentation deep network. To validate the network, we examined a replication cohort of 1089 whole slide images from 419 patients derived from multiple institutions. Findings. PECNet segmented both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation was able to quantitate intact eosinophils with a mean absolute error of 0.611 eosinophils and classify EoE disease activity with an accuracy of 98.5%. Using whole slide images from the validation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%, and specificity of 95.14% in reporting EoE disease activity. Interpretation. We have developed a deep learning multi-label semantic segmentation network that successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges.
Internal Category with Families in Presheaves
Venue: arXiv cs.LO 2021
Year: 2021

In this note, we review a construction of category with families (CwF) in a presheaf category. When the base category of a presheaf category is a CwF, we internalize this CwF structure in the CwF of the presheaf category. This note assumes working knowledge on category theory.
Convergence and Inequality in Research Globalization
Venue: arXiv cs.CY 2021
Year: 2021

The catch-up effect and the Matthew effect offer opposing characterizations of globalization: the former predicts an eventual convergence as the poor can grow faster than the rich due to free exchanges of complementary resources, while the latter, a deepening inequality between the rich and the poor. To understand these effects on the globalization of research, we conduct an in-depth study based on scholarly and patent publications covering STEM research from 218 countries/regions over the past four decades, covering more than 55 million scholarly articles and 1.7 billion citations. Unique to this investigation is the simultaneous examination of both the research output and its impact in the same data set, using a novel machine learning based measure, called saliency, to mitigate the intrinsic biases in quantifying the research impact. The results show that the two effects are in fact co-occurring: there are clear indications of convergence among the high income and upper middle income countries across the STEM fields, but a widening gap is developing that segregates the lower middle and low income regions from the higher income regions. Furthermore, the rate of convergence varies notably among the STEM sub-fields, with the highly strategic area of Artificial Intelligence (AI) sandwiched between fields such as Medicine and Materials Science that occupy the opposite ends of the spectrum. The data support the argument that a leading explanation of the Matthew effect, namely, the preferential attachment theory, can actually foster the catch-up effect when organizations from lower income countries forge substantial research collaborations with those already dominant. The data resoundingly show such collaborations benefit all parties involved, and a case of role reversal can be seen in the Materials Science field where the most advanced signs of convergence are observed.
A 3D Printing Hexacopter: Design and Demonstration
Venue: arXiv cs.RO 2021
Year: 2021

3D printing using robots has garnered significant interest in manufacturing and construction in recent years. A robot's versatility paired with the design freedom of 3D printing offers promising opportunities for how parts and structures are built in the future. However, 3D printed objects are still limited in size and location due to a lack of vertical mobility of ground robots. These limitations severely restrict the potential of the 3D printing process. To overcome these limitations, we develop a hexacopter testbed that can print via fused deposition modeling during flight. We discuss the design of this testbed and develop a simple control strategy for initial print tests. By successfully performing these initial print tests, we demonstrate the feasibility of this approach and lay the groundwork for printing 3D parts and structures with drones.
Computing Sum of Sources over a Classical-Quantum MAC
Venue: arXiv cs.IT 2021
Year: 2021

We consider the problem of communicating a general bivariate function of two classical sources observed at the encoders of a classical-quantum multiple access channel. Building on the techniques developed for the case of a classical channel, we propose and analyze a coding scheme based on coset codes. The proposed technique enables the decoder recover the desired function without recovering the sources themselves. We derive a new set of sufficient conditions that are weaker than the current known for identified examples. This work is based on a new ensemble of coset codes that are proven to achieve the capacity of a classical-quantum point-to-point channel.
A Computational Design and Evaluation Tool for 3D Structures with Planar Surfaces
Venue: arXiv cs.GR 2021
Year: 2021

Three dimensional (3D) structures composed of planar surfaces can be build out of accessible materials using easier fabrication technique with shorter fabrication time. To better design 3D structures with planar surfaces, realistic models are required to understand and evaluate mechanical behaviors. Existing design tools are either effort-consuming (e.g. finite element analysis) or bounded by assumptions (e.g. numerical solutions). In this project, We have built a computational design tool that is (1) capable of rapidly and inexpensively evaluating planar surfaces in 3D structures, with sufficient computational efficiency and accuracy; (2) applicable to complex boundary conditions and loading conditions, both isotropic materials and orthotropic materials; and (3) suitable for rapid accommodation when design parameters need to be adjusted. We demonstrate the efficiency and necessity of this design tool by evaluating a glass table as well as a wood bookcase, and iteratively designing an origami gripper to satisfy performance requirements. This design tool gives non-expert users as well as engineers a simple and effective modus operandi in structural design.
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks
Venue: arXiv cs.CV 2021
Year: 2021

Low-quality face image restoration is a popular research direction in today's computer vision field. It can be used as a pre-work for tasks such as face detection and face recognition. At present, there is a lot of work to solve the problem of low-quality faces under various environmental conditions. This paper mainly focuses on the restoration of motion-blurred faces. In increasingly abundant mobile scenes, the fast recovery of motion-blurred faces can bring highly effective speed improvements in tasks such as face matching. In order to achieve this goal, a deblurring method for motion-blurred facial image signals based on generative adversarial networks(GANs) is proposed. It uses an end-to-end method to train a sharp image generator, i.e., a processor for motion-blurred facial images. This paper introduce the processing progress of motion-blurred images, the development and changes of GANs and some basic concepts. After that, it give the details of network structure and training optimization design of the image processor. Then we conducted a motion blur image generation experiment on some general facial data set, and used the pairs of blurred and sharp face image data to perform the training and testing experiments of the processor GAN, and gave some visual displays. Finally, MTCNN is used to detect the faces of the image generated by the deblurring processor, and compare it with the result of the blurred image. From the results, the processing effect of the deblurring processor on the motion-blurred picture has a significant improvement both in terms of intuition and evaluation indicators of face detection.
QoS-Driven Resource Optimization for Intelligent Fog Radio Access Network: A Dynamic Power Allocation Perspective
Venue: arXiv cs.IT 2021
Year: 2021

The fog radio access network (Fog-RAN) has been considered a promising wireless access architecture to help shorten the communication delay and relieve the large data delivery burden over the backhaul links. However, limited by conventional inflexible communication design, Fog-RAN cannot be used in some complex communication scenarios. In this study, we focus on investigating a more intelligent Fog-RAN to assist the communication in a high-speed railway environment. Due to the train's continuously moving, the communication should be designed intelligently to adapt to channel variation. Specifically, we dynamically optimize the power allocation in the remote radio heads (RRHs) to minimize the total network power cost considering multiple quality-of-service (QoS) requirements and channel variation. The impact of caching on the power allocation is considered. The dynamic power optimization is analyzed to obtain a closed-form solution in certain cases. The inherent tradeoff among the total network cost, delay and delivery content size is further discussed. To evaluate the performance of the proposed dynamic power allocation, we present an invariant power allocation counterpart as a performance comparison benchmark. The result of our simulation reveals that dynamic power allocation can significantly outperform the invariant power allocation scheme, especially with a random caching strategy or limited caching resources at the RRHs.
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time
Venue: arXiv cs.DB 2021
Year: 2021

We propose opportunistic evaluation, a framework for accelerating interactions with dataframes. Interactive latency is critical for iterative, human-in-the-loop dataframe workloads for supporting exploratory data analysis. Opportunistic evaluation significantly reduces interactive latency by 1) prioritizing computation directly relevant to the interactions and 2) leveraging think time for asynchronous background computation for non-critical operators that might be relevant to future interactions. We show, through empirical analysis, that current user behavior presents ample opportunities for optimization, and the solutions we propose effectively harness such opportunities.
Off-Chain Micropayment Pool for High-ThroughputBandwidth Sharing Rewards
Venue: arXiv cs.DC 2021
Year: 2021

This paper presents a layer-2 micropayment pool design which supports high-throughput blockchain off-chain payment, designed specifically for the use case of rewarding users who share redundant bandwidth to assist video stream delivery. We analyze the validity and effectiveness of the proposed micropayment pool. Our analysis results demonstrate that the proposed micropayment pool design is better suited for the bandwidth reward use case compared to existing off-chain payment channel solution.
Semantic constraints to represent common sense required in household actions for multi-modal Learning-from-observation robot
Venue: arXiv cs.RO 2021
Year: 2021

The paradigm of learning-from-observation (LfO) enables a robot to learn how to perform actions by observing human-demonstrated actions. Previous research in LfO have mainly focused on the industrial domain which only consist of the observable physical constraints between a manipulating tool and the robot's working environment. In order to extend this paradigm to the household domain which consists non-observable constraints derived from a human's common sense; we introduce the idea of semantic constraints. The semantic constraints are represented similar to the physical constraints by defining a contact with an imaginary semantic environment. We thoroughly investigate the necessary and sufficient set of contact state and state transitions to understand the different types of physical and semantic constraints. We then apply our constraint representation to analyze various actions in top hit household YouTube videos and real home cooking recordings. We further categorize the frequently appearing constraint patterns into physical, semantic, and multistage task groups and verify that these groups are not only necessary but a sufficient set for covering standard household actions. Finally, we conduct a preliminary experiment using textual input to explore the possibilities of combining verbal and visual input for recognizing the task groups. Our results provide promising directions for incorporating common sense in the literature of robot teaching.